from typing import Dict, Any, List
import cv2
import numpy as np
from app.utils.logger import get_logger

logger = get_logger(__name__)


# 图表解析模块

class ChartParserModule:
    _instance = None
    _initialized = False

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(ChartParserModule, cls).__new__(cls)
        return cls._instance

    def __init__(self):
        if not self._initialized:
            logger.info("Initializing ChartParserModule")
            self._initialized = True

    def parse(self, image_data: bytes) -> Dict[str, Any]:
        """
        解析图表内容

        Args:
            image_data: 图像字节数据

        Returns:
            图表解析结果
        """
        try:
            # 将字节数据转换为OpenCV图像
            nparr = np.frombuffer(image_data, np.uint8)
            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

            # 图表类型识别（模拟）
            chart_type = self._classify_chart_type(img)

            # 图表元素提取
            chart_elements = self._extract_chart_elements(img)

            # 数据提取（模拟）
            chart_data = self._extract_chart_data(img)

            return {
                "chart_type": chart_type,
                "elements": chart_elements,
                "data": chart_data,
                "confidence": 0.85
            }

        except Exception as e:
            raise RuntimeError(f"Chart parsing failed: {str(e)}")

    def _classify_chart_type(self, img) -> str:
        """分类图表类型"""
        # 模拟实现，实际应使用分类模型
        return "bar_chart"  # bar_chart, line_chart, pie_chart, scatter_plot等

    def _extract_chart_elements(self, img) -> List[Dict]:
        """提取图表元素"""
        # 模拟图表元素
        return [
            {"type": "axis", "position": "x", "bbox": [50, 400, 350, 420]},
            {"type": "axis", "position": "y", "bbox": [30, 50, 50, 380]},
            {"type": "legend", "bbox": [380, 50, 450, 150]}
        ]

    def _extract_chart_data(self, img) -> Dict[str, Any]:
        """提取图表数据"""
        # 模拟数据提取
        return {
            "series": [
                {
                    "name": "Series 1",
                    "data": [10, 25, 30, 45, 20],
                    "color": "#FF0000"
                },
                {
                    "name": "Series 2",
                    "data": [15, 30, 20, 40, 35],
                    "color": "#00FF00"
                }
            ],
            "categories": ["A", "B", "C", "D", "E"]
        }
